Ruprecht-Karls-Universität Heidelberg
Siegel der Universität Heidelberg

Module for [Scientific Computing]

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[Statistical Forecasting] - [2015 Sommer]

Module Code
Statistical Forecasting
Credit Points
8 CP
1 semester
Methods Lecture 4 h + Exercise course 2 h
Objectives To have a firm understanding of the statistical theory of forecasting, and the ability to design, implement and evaluate prediction techniques
Content • Basic notions: statistical decision theory, probabilistic and point forecasts, prediction spaces, information bases, calibration and sharpness • Proper scoring rules and consistent scoring functions • Forecasts combinations • Times series forecasts and spatial prediction • Statistical postprocessing of ensemble forecasts; combining numerical and statistical approaches • Applications and case studies in meteorology, economics and other disciplines
Learning outcomes • Firm theoretical understanding of the measure theoretic, probabilistic and statistical foundations of forecasting • Design and implementation of statistical forecasting algorithms, along with associated assessment techniques
Suggested previous knowledge MC4 or equivalent; MD2 or equivalent;
Programming in R
Assessments TBD (typically, homework and written exam)
Literature Gneiting, T.: Making and evaluating point forecasts. Journal of the American Statistical Association 106 (2011), 746–762.
Gneiting, T. and Raftery, A. E.: Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 102 (2007), 359–378.
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